'''simple docstring'''




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[int]:
    __lowerCamelCase            =    OmegaConf.load(UpperCamelCase__							)
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)['''model''']
    __lowerCamelCase            =    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    __lowerCamelCase            =    {}
    __lowerCamelCase            =    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            __lowerCamelCase            =    state_dict[key]

    # extract state_dict for UNetLDM
    __lowerCamelCase            =    {}
    __lowerCamelCase            =    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            __lowerCamelCase            =    state_dict[key]

    __lowerCamelCase            =    config.model.params.first_stage_config.params
    __lowerCamelCase            =    config.model.params.unet_config.params

    __lowerCamelCase            =    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    __lowerCamelCase            =    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    __lowerCamelCase            =    DDIMScheduler(
        timesteps=config.model.params.timesteps							,    beta_schedule='''scaled_linear'''							,    beta_start=config.model.params.linear_start							,    beta_end=config.model.params.linear_end							,    clip_sample=UpperCamelCase__							,    )

    __lowerCamelCase            =    LDMPipeline(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", type=str, required=True)
  parser.add_argument("--config_path", type=str, required=True)
  parser.add_argument("--output_path", type=str, required=True)
  __UpperCAmelCase					=parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)

'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase					={
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["EfficientNetImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientNetForImageClassification",
      "EfficientNetModel",
      "EfficientNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure)
